Cooking by utilizing a cluster analysis and cooking utensils therefor

ABSTRACT

The invention relates to a method for cooking items that are to be cooked in a cooking chamber of a cooking utensil comprising a control and/or regulating mechanism which has access to primary, chronologically arranged measured data and secondary measured data of a state variable regarding at least one item that is to be cooked and/or a state variable regarding a cooking utensil, said secondary measured data not being chronologically arranged. The cooking process is carried out in the following steps according to said state variable/s: an actual cooking curve of the measured data Z, of the item (i) that is to be cooked is detected up to a specific point in time t&lt;SB&gt;M&lt;/SB&gt; before the final cooking point t&lt;SB&gt;E&lt;/SB&gt; is reached; the detected actual cooking curve Z i  is associated with a representative cooking profile X n  that is determined via a cluster analysis; and the cooking process is carried out according to the determined representative cooking profile X n , wherein i and n∈|N. Also disclosed is a cooking utensil comprising a cooking chamber for receiving items that are to be cooked, a measuring device for detecting measuring data, a device for storing measured data and variables that are determined therefrom, and a control and/or regulating mechanism for directing the cooking process according to the inventive method.

RELATED APPLICATIONS

This is the U.S. national phase of International Application No. PCT/DE2004/000003 filed Jan. 7, 2004, the entire disclosure of which is incorporated herein by reference and claims priority to German application 103 00 465.3 filed Jan. 9, 2003.

FIELD OF DISCLOSURE

The invention concerns a method for cooking of items to be cooked in a cooking chamber of cooking apparatus with a control and/or regulation device, which has access to primary, measured data arranged according to time for at least one variable relating to an item to be cooked and/or cooking apparatus, as well as secondary measured data which are not ordered according to time, the cooking process being guided depending on these data, as well as a cooking apparatus with which such a method can be realized.

For example, a method is known from DE 197 18 399 A1 for the cooking of an item to be cooked in a cooking apparatus with a cooking chamber as well as a measuring device is known for recording at least one state variable of an item to be cooked and/or cooking apparatus, the cooking process being guided as a function of these. In this known method, the time of the end of the cooking process is determined based on values of at least one state variable of the item to be cooked, which is determined during the cooking process at different times, that is, not in test steps, as well as based on a predetermined end value of the state variable of the item to be cooked at the end of the cooking process, whereby the duration of time until reaching a specified end state of a said end value of the state of the item to be cooked is extrapolated and the extrapolation is based on the previous cooking process.

It is known from DE 196 09 116 A1 that, in a test step, the core temperature is scanned several times in succession until a defined point in time and with simultaneous consideration of the cooking chamber temperature belonging to the scanned core temperature, based on the scanned values, one can determine the end point in time at which such a target core temperature should be reached. This determination is done by solution of differential equations.

In U.S. Pat. No. 5,352,866 the determination of a cooking time is described more or less according to the principles of a proportional differential and integral regulator, based on stored time-temperature curves.

It is known from U.S. Pat. No. 4,970,359 and U.S. Pat. No. 4,682,013 that the cooking process can be divided into different sections, whereby measurement is performed up to a special point and then a regulation takes place based on special formulas and stored measured data.

In GB 2 203 320 A1 a comparison with a stored model curve is addressed generally.

It is known from EP 0 701 387 A2 to use a characteristic vector for the determination of a cooking course, which takes into consideration a maximum rate of change of moisture content, the value of the moisture content at its maximum rate of change, the time required to reach a certain moisture content value, and/or the mean moisture between two different points.

EP 0 550 312 A2 describes the control of a cooking process through briefly stored data.

In many areas of daily living and scientific research, it becomes necessary to divide as a rule, large sets of objects with the aid of test and experimental results, that is, properties, into a generally small number of groups, classes, clusters, heaps or the like, or to combine individual elements of the set of objects to groups, so that the individual groups are as homogeneous as possible but the differences between the groups are as large as possible. In this way such existing structures of the set of objects can be made recognizable and interpretable. Thus, for example, cluster analysis has already been used in empirical sciences, such as psychology, medicine, biology, geology, criminology and business management, see, for example, “Short-term simultaneous planning of sales, production and purchasing program in module-dependent seasonal undertakings as adaptive process” by Dr. Günter Blaschke, which appeared in Peter Lang, in the series of V Volks and Betriebswirtschaft, Volume 218, 1979. The task of cluster analysis is based on the fact that a set of objects, that is, carriers of variables, is given, each one of which has a certain measured value, that is, a manifestation of variable, with a number of properties, and groupings of the objects are searched for in which similarity exists in all properties to some degree.

In spite of the numerous known cooking process performance methods, there is still a demand for an optimized operation method.

SUMMARY OF THE DISCLOSURE

Therefore, it is the task to provide an optimized method for individual operation of a cooking process.

This task is solved by the following steps:

-   -   Acquiring of the actual course of the cooking process with         measured data Z_(i) of the item to be cooked i to a certain time         point t_(M) before the cooking completion time t_(E),     -   Assignment of the acquired actual course of the cooking process         Z_(i) to a representative cooking process X_(n) determined by         cluster analysis, and     -   Guiding the cooking process as a function of the determined         representative cooking profile X_(n), where i and n∈|N

Hereby, it is proposed that in the cluster analysis at least one regression, one similarity comparison, a coefficient comparison, formation of a region, an interpolation and/or extrapolation is performed.

Here, the cluster analysis includes a standardization or making comparable the actual courses of the cooking process, especially by determination of an accumulated percentage Y_(i) of the 100% end value of measured data Z_(i) at the cooking completion time t_(E) as a function of t_(j) with j∈N where the 100% end value is preferably defined as the target quantity Z_(i)(t_(E)) at the cooking completion time t_(E).

Here, it is proposed that the target quantity Z_(i)(t_(E)) be set or taken from a stored representative cooking profile X_(n).

Again, it can be provided that the cluster analysis includes a classification of actual courses of the cooking process which were made comparable, especially by

-   -   Determination of the squares of the distance A_(u) to the         cooking completion time t_(E) with l∈|N, where         ${{A_{il}\left( t_{E} \right)} = {\sum\limits_{j = 0}^{E}\left( {{Y_{i}\left( t_{j} \right)} - {Y_{i}\left( t_{j} \right)}} \right)^{2}}},$     -   Determination of a number N of classes K_(n) with n={1, 2 . . .         N} and     -   Defining each class K_(n) by a single or a respective maximum         value of the square of the distance A_(u)(t_(j)).

Here, the cluster analysis includes an assignment of a representative cooking profile X_(n)(t_(j)) to each class K_(n), where a representative cooking profile X_(n)(t_(j)) is preferably determined by calculation of the mean value and/or of the centroid of the accumulated percentages Y_(i)(t_(j)) per class K_(n) or is called up from a storage device.

A preferred embodiment of the cooking method is characterized in that the assignment of an actual course of the cooking process Z_(i) to a representative cooking profile X_(n)(t_(j)) of a class K_(n) includes the following:

-   -   Determination of a value of deviation S_(in)(t_(M)) for each         class K_(n) within a         ${S_{in}\left( t_{M} \right)} = {\sum\limits_{j = 0}^{M}\left( {\left( \frac{Y_{i}\left( t_{j} \right)}{X_{n}\left( t_{j} \right)} \right) - \left( \frac{Y_{i}\left( t_{j + 1} \right)}{X_{n}\left( t_{j + 1} \right)} \right)} \right)^{2}}$         with  X_(n) > 0,  and     -   Obtaining of the value of deviation S_(in)(t_(M)) with the         smallest value for the percentage Y_(i)(t_(M)) at time t_(M).

Further, it can be provided that the guiding of the cooking process includes a prediction of measured data Z_(i)(t_(E)) at the cooking completion time t_(E) through the determined representative cooking profile X_(n)(t_(j)).

Here, it is proposed that the prediction include the following steps:

-   -   Assignment of the accumulated percentage Y_(i)(t_(j)) to a class         K_(n) and thus to a representative cooking profile X_(n)(t_(j)),     -   Obtaining of a multiplier P_(in) at the time t_(M), where         $P_{in} = \frac{Z_{i}\left( t_{M} \right)}{X_{n}\left( t_{M} \right)}$         where  X_(n) > 0, and     -   Extrapolation by multiplication of the end value X_(n)(t_(E)) of         the representative cooking profile with the multiplier, where         X _(n)(t _(E))P _(in) =Z _(i)(t _(E)).

Furthermore, it is proposed that the weight loss of the item to be cooked, the core temperature of the item to be cooked, the diameter of the item to be cooked, the density of the item to be cooked, the type of item to be cooked, the degree of ripeness of the item to be cooked, the pH value of the item to be cooked, the consistency of the item to be cooked, the state of storage of the item to be cooked, the odor of the item to be cooked, the taste of the item to be cooked, the quality of the item to be cooked, the browning of the item to be cooked, the crust formation of the item to be cooked, the vitamin degradation of the item to be cooked, the formation of carcinogenic substances in the item to be cooked, the hygiene of the item to be cooked, the water activity of the item to be cooked, the moisture content of the item to be cooked, the protein content of the item to be cooked and the thermal conductivity of the item to be cooked each represent a state variable of the item to be cooked, which is obtained from the primary measured data.

It can be provided that the temperature in the cooking chamber, the humidity in the cooking chamber, and the air movement rate in the cooking chamber each represent a state variable of the cooking apparatus, which is obtained from the primary measured data.

It can also be provided that as secondary measured data, at least an apparatus input by a user, including the selection of a cooking program and/or a state variable of the item to be cooked at the end of the cooking time and/or state variable of the cooking apparatus at the end of the cooking time and/or at least an external circumstance, such as date, time, season, weather and/or geographic location is or are determined.

It is also proposed that each measured actual cooking course Z_(i), each accumulated determined percentage Y_(i) and/or each determined representative cooking profile X_(n) is stored in a memory device, automatically or optionally.

Additionally, a cooking apparatus with a cooking chamber includes a measuring device for determining the measured data, a memory device for storing measured data and quantities determined from them and a control and/or regulating device for guiding a cooking process according to the method described above.

Hereby it can be provided that the measuring device and the control and/or regulating device is made into one, preferably also including the memory device, especially integrated in a cooking process sensor.

Thus, the cooking method and apparatus is based on the surprising finding that with the aid of cluster analysis, which is essentially known from empirical social research, cooking methods can be condensed, typified, profiled and mathematically unequivocally characterized as well as described in spite of its highly complex relationship of the items. As a result, the problem is reduced to a few key quantities to be defined, instead of isolation and consideration of an infinite number of individual influential quantities. In particular, new actual courses of the cooking process can be recognized early with the aid of their typical manifestations and can be assigned automatically to a representative cooking profile. Thus, an additional step is made in the direction of fully automatic cooking.

The totality of a set of data that belong to a cooking process, which represent a data packet, which contains not only primary measured data arranged in time, such as the weight loss of an item to be cooked, the core temperature of an item to be cooked, the humidity in the cooking chamber, the cooking chamber temperature or similar, but also secondary data, for example, the weather, the date, the geographic location or similar, which provide additional help in the automatic recognition of a useful goal. A cluster can be formed only when a sufficient amount of data packets which fulfill the common criteria for a cluster exist. Several mathematical methods can be used for clustering, such as regression, similarity comparison, interpolation and extrapolation or similar. When a comparison fulfills the typical common criteria, the cooking process is considered to be recognized. In addition, in case of positive comparison results, after conversion into a format typical to the cluster, the data packet can be added to the cluster so that a self-learning process takes place.

BRIEF DESCRIPTION OF THE DRAWINGS

Other characteristics and advantages of the cooking method and apparatus follow from the description given below, in which a practical example of the cooking method and apparatus is explained in detail with the aid of schematic drawings. The following are shown:

FIGS. 1 a and 1 b A typical expression of the characteristics or a typical progress of primary measured data as a function of the cooking time;

FIG. 2 The accumulated percentage of an actual course of the cooking process based on the predetermined 100% end value, as a function of the cooking time to the cooking completion time;

FIG. 3 Two curves according to FIG. 2 to explain the classification used in a cooking method; and

FIG. 4 an actual course of the cooking process in comparison to a representative cooking profile for illustration of the operation of a cooking process.

DETAILED DESCRIPTION

FIG. 1 a shows actual courses of the cooking process for different items to be cooked i, which are assigned to a class K_(n) and, for example, only primary measured data Z_(i), such as in the form of the weight loss of an item to be cooked are represented. FIG. 1 b shows actual courses of the cooking process as primary measured data Z_(i) for a number of items to be cooked i, of a second class Z_(b), analogously to FIG. 1 a. FIGS. 1 a and 1 b illustrate using the course of primary measured data Z_(i), in a comparison, how different cooking process characteristics can be expressed for the cooking time t_(j) for two different items to be cooked classes K_(a) and K_(b). At the same time, however, FIGS. 1 a and 1 b also show that different items to be cooked i, within a class K_(a) and K_(b), respectively, do not necessarily have to have different actual courses of the cooking process with regard to a state variable Z_(i) of an item to be cooked, such as in the form of the weight loss of the item to be cooked i. Rather, actually items to be cooked can be assigned to certain classes of items to be cooked K_(n) for which a certain cooking profile X_(n) is typical with regard to one or more measured criteria, especially the state variable Z_(i) for the item to be cooked. In the following, it is assumed that the state variable Z_(i) of the item to be cooked is the weight loss Z_(i) of the item to be cooked i.

The actual courses of the cooking process of the weight loss Z_(i) of different items to be cooked i must be made comparable to one another within the framework according to the cluster analysis. For this reason, the actual courses of the cooking process must be represented in a form from which the characteristics of this can be derived. For this purpose, according to the invention the determination of the accumulated percentage Y_(i), for example, of a 100% end value of the weight loss Z_(i)(t_(E)) which is given as target value at the cooking completion time T_(E) is suitable. The target value can be entered, for example, for a table manually for the particular item to be cooked in cooking apparatus not shown here. In FIG. 2 an accumulated percentage Y_(i)(t_(j)) for the item to be cooked i is represented as a function of time t_(j).

In order to be able to use cluster analysis in the operation of a cooking process, a sensible measure of the similarity must be defined. For this purpose, the complete cooking time until the cooking completion time t_(E) is divided into a suitable number of intervals j, for example, 10 intervals, and for each time interval t_(j) a sum of the square of distance A_(il) of two accumulated percentage curves Y_(i)(t_(j)) and Y_(l)(t_(j)) is calculated, namely as follows ${A_{il}\left( t_{E} \right)} = {\sum\limits_{j = 0}^{E}{\left( {{Y_{i}\left( t_{j} \right)} - {Y_{i}\left( t_{j} \right)}} \right)^{2}.}}$

FIG. 3 illustrates the determination of the sum of the square of distance A_(il)(t_(E)) from the differences of the two accumulated percentage curves Y_(i)(t_(j)) and Y_(l)(t_(j)) for each time interval t_(j), after squaring and summation.

Depending on a sensible number N of classes K_(n) for the items to be cooked i, for example 10 classes, a permissible maximum value for the sum of the square of distance A_(il)(t_(E)) must be determined for dividing the actual courses of the cooking process Z_(i) into different classes K_(n), so that in a class K_(n) all those items to be cooked i, are included for which the sum of the square distance A_(il)(t_(E)) are below the maximum value. Then, within a class K_(n) there is a great degree of similarity of the course of the weight loss Z_(i), while between the classes K_(n) there is a small degree of similarity. For each class K_(n) a representative is also determined, that is, a representative cooking profile X_(n)(t_(j)), from which the centroid of the percentages Y_(i)(t_(j)) for each time interval t_(j) is determined.

After the determination of the classes K_(n) and determination of the representative cooking profile X_(n)(t_(j)) according to cluster analysis, including storage, for each item to be cooked i independently of the cooking apparatus, originally a comparison can be made between a detected actual course of the cooking process and the stored representative cooking profiles X_(n)(t_(j)) in order to operate the cooking process. For this purpose, for each class K_(n) within a determined time interval t_(M) with t_(M)<t_(E), a deviation value S_(in)(t_(M)) is determined as follows: ${S_{in}\left( t_{M} \right)} = {\sum\limits_{j = 0}^{M}{\left( {\left( \frac{Y_{i}\left( t_{j} \right)}{X_{n}\left( t_{j} \right)} \right) - \left( \frac{Y_{i}\left( t_{j + 1} \right)}{X_{n}\left( t_{j + 1} \right)} \right)} \right)^{2}.}}$

The per actual course of the cooking process, represented as accumulate percentage Y_(i)(t_(j)) to the end value t_(E), the representative cooking profile X_(n)(t_(j)) is selected from all the deviation values S_(in)(t_(M)) at which the deviation value S_(in)(t_(M)) is the smallest. As a result of this assignment, now the end value of the detected state variables Z_(i)(t_(E)), that is, of the weight loss, can be predicted with the aid of the corresponding representative cooking profile X_(n)(t_(j)). It can be assumed here that the actual course of the cooking process Y_(j)(t_(M)) detected at the time t_(M), that is, an incomplete course, and the representative cooking profile X_(n)(t_(M)) determined at the time t_(M) have a similar course to the cooking completion time t_(E), namely, they differ exclusively by a multiplication constant P_(in). This constant P_(in) at time t_(M) is calculated as follows: $P_{in} = {\frac{Y_{i}\left( t_{M} \right)}{X_{n}\left( t_{M} \right)}.}$

Finally, in order to operate the cooking process according to the invention, only the determined constant P_(in) needs to be multiplied with the value of the representative cooking profile X_(n) at the cooking completion time t_(E), namely, as follows: X _(n)(t _(E))P _(in) =Y _(i)(t _(E)).

The just-described prediction of a state variable Z_(i), such as the weight loss of the item to be cooked i at the cooking completion time t_(E) after the acquiring of an actual course of the cooking process to time t_(M) and assignment of this to a representative cooking profile is illustrated in FIG. 4, where the selected representative cooking profile X_(n) was recalculated from the representation as an accumulated percentage to a representative course of the weight loss X′_(n).

Thus, in summary, it can be stated that with a cluster analysis an automatic recognition of product and operation of cooking process is possible by assignment of an actual course of the cooking process which is measured at a time t_(M) to a previously-determined representative cooking profile.

The characteristics of the invention which are disclosed in the above specification, in the claims as well as in the drawing, can be essential both individually as well as in any arbitrary combination for the realization of the invention in its different embodiments. 

1. Method for cooking of items in a cooking chamber of a cooking apparatus with a control or regulation device, which has access to primary measured data arranged in time as well as to secondary measured data which are not arranged in time, of at least one state variable of an item to be cooked or of the cooking apparatus, the method comprising: acquiring an actual course of a cooking process of the measured data Z_(i) of the item to be cooked i up to a predefined time t_(M) before a cooking completion time t_(E), assigning the acquired actual course of cooking process Z_(i) to a representative cooking profile X_(n) determined through cluster analysis, where the cluster analysis includes standardization of the actual cooking courses or making them comparable, and guiding the cooking process as a function of the determined representative cooking profile X_(n), where i and n∈|N.
 2. Method according to claim 1, wherein the cluster analysis includes at least one of a regression analysis, or a similarity comparison, or a coefficient comparison, or a formation of a range, or an interpolation or an extrapolation.
 3. Method according to claim 1, wherein the actual cooking courses are standardized or made comparable, by determining an accumulated percentage Y_(i) from the 100% end value of the measured data Z_(i) at the cooking completion time t_(E) as a function of time t_(j) with j∈|N, where the 100% end value is defined as a target quantity Z_(i)(t_(E)) at the cooking completion time t_(E).
 4. Method according to claim 3, wherein the target quantity Z_(i)(t_(E)) is adjusted or taken from a stored representative cooking profile X_(n).
 5. Method according to claim 1, wherein the cluster analysis includes classifying actual courses of the cooking process made comparable, including, determining the sums of the squares of a distance A_(il) to the end time of cooking t_(E) with l∈|N, where ${{A_{il}\left( t_{E} \right)} = {\sum\limits_{j = 0}^{E}\left( {{Y_{i}\left( t_{j} \right)} - {Y_{i}\left( t_{j} \right)}} \right)^{2}}},$ determining a number N of classes K_(n) with n={1, 2 . . . N}, and defining each class K_(n) through a single or a respective maximum value of the sum of the squares of the distance A_(ii)(t_(E)).
 6. Method according to claim 5, wherein the cluster analysis includes the assignment of a representative cooking profile X_(n)(t_(j)) to each class K_(n).
 7. Method according to claim 6, wherein the assignment of an actual course of the cooking process Z_(i) to a representative cooking profile X_(n)(t_(j)) of a class K_(n) includes: determining a value of deviation S_(in)(t_(M)) for each class K_(n) within a time interval t_(M), where ${{S_{in}\left( t_{M} \right)} = {{\sum\limits_{j = 0}^{M}{\left( {\left( \frac{Y_{i}\left( t_{j} \right)}{X_{n}\left( t_{j} \right)} \right) - \left( \frac{Y_{i}\left( t_{j + 1} \right)}{X_{n}\left( t_{j + 1} \right)} \right)} \right)^{2}\quad{with}\quad X_{n}}} > 0}},{and}$ obtaining the value of deviation S_(in)(t_(M)) with the value of the smallest magnitude for the percentage Y_(i)(t_(M)) at time t_(M).
 8. Method according to claim 1, wherein the guiding of the cooking process includes predicting measured data Z_(i)(t_(E)) at an end of cooking time t_(E) through the determined representative cooking profile X_(n)(t_(j)).
 9. Method according to claim 8, wherein predicting includes: assigning the accumulated percentages Y_(i)(t_(j)) to a class K_(n) and thus to a representative cooking profile X_(n)(t_(j)), obtaining a multiplier P_(in) at time t_(M) where ${P_{in} = \frac{Z_{i}\left( t_{M} \right)}{X_{n}\left( t_{M} \right)}},\quad{{{where}\quad X_{n}} > {0\quad{and}}}$ extrapolating by multiplication of the end value X_(n)(t_(E)) of the representative cooking profile with the multiplier P_(in), where X _(n)(t _(E))P _(in) =Z _(i)(t _(E))
 10. Method according to claim 1, wherein a state variable of the item to be cooked obtained from the primary measured data includes one or more of the weight loss of the item to be cooked, the core temperature of the item to be cooked, the diameter of the item to be cooked, the density of the item to be cooked, the type of item to be cooked, the degree of ripeness of the item to be cooked, the pH value of the item to be cooked, the consistency of the item to be cooked, the state of storage of the item to be cooked, the odor of the item to be cooked, the taste of the item to be cooked, the quality of the item to be cooked, the browning of the item to be cooked, the crust formation of the item to be cooked, the vitamin degradation of the item to be cooked, the formation of carcinogenic substances in the item to be cooked, the hygiene of the item to be cooked, the water activity of the item to be cooked, the moisture content of the item to be cooked, the protein content of the item to be cooked, or the thermal conductivity of the item to be cooked.
 11. Method according to claim 1, wherein a cooking apparatus state variable determined from the primary measured data includes one or more of the temperature in the cooking chamber, or the moisture in the cooking chamber or the air movement rate in the cooking chamber.
 12. Method according to claim 1, including determining, as secondary measured data, at least one apparatus input by a user.
 13. Method according to claim 1, wherein each measured actual course of the cooking process Z_(i), each determined accumulated percentage Y_(i) or each determined representative cooking profile X_(n) is stored in a memory device, automatically or optionally.
 14. Cooking apparatus with a cooking chamber for holding an item to be cooked, comprising: a measuring device for determining measured data, a memory device for storing measured data and quantities determined from the measured data, and a control or regulation device for guiding a cooking process by, acquiring an actual course of a cooking process of measured data of the item to be cooked up to a predefined time before a cooking completion time, assigning the acquired actual course of the cooking process to a representative cooking profile determined through cluster analysis, and guiding the cooking process as a function of the determined representative cooking profile.
 15. Cooking apparatus according to claim 14, wherein the measuring device and the control or regulation device are constructed as one.
 16. Cooking apparatus according to claim 15, wherein the memory device is integrated with the measuring device and the control or regulation device.
 17. Cooking apparatus according to claim 16, wherein the memory device, the measuring device and the control or regulation device are integrated in a cooking process sensor.
 18. Method according to claim 1, wherein the cluster analysis includes classifying actual cooking courses made comparable.
 19. Method according to claim 1 wherein the cluster analysis includes a standardization of actual courses of the cooking process or making them comparable by determining an accumulated percentage Y_(i) from the 100% end value of the measured data Z_(i)(t_(E)) at the cooking completion time t_(E) as a function of time t_(j) with j∈|N.
 20. Method according to claim 6, wherein a representative cooking profile X_(n)(t_(j)) is determined by calculating a mean value or a centroid of the accumulated percentages Y_(i)(t_(j)) per class K_(n) or the representative cooking profile X_(n)(t_(j)) is recalled from a memory device.
 21. Method according to claim 12, wherein the at least one apparatus input by a user includes the selection of a cooking program or a state variable of an item to be cooked at the end of the cooking, time or a state variable of the cooking apparatus at the end time of cooking time.
 22. Method according to claim 12, wherein the external factor includes one or more of a date, or a time, or a season, or a weather or a geographical location. 